Assessment of Global Forest Coverage through Machine Learning Algorithms

Authors

  • P S Metkewar Symbiosis International University image/svg+xml
  • Ravi Chauhan Symbiosis International University image/svg+xml
  • A Prasanth Sri Venkateswara College of Engineering
  • Malathy Sathyamoorthy KPR Institute of Engineering and Technology image/svg+xml

DOI:

https://doi.org/10.4108/eetsis.5122

Keywords:

Forest Coverage, Deforestation, Remote Sensing, Ground Surveys, Environmental Issues, Climate Change, Machine Learning, Mean Squared error, R2 Score, Mean Absolute Error, Root Mean Square Error

Abstract

This exploration of paper presents an investigation of the Forest Region Inclusion Dataset that gives data on the backwoods inclusion of different nations overall from 1990 to 2020. The dataset contains country-wise information on population, population density, population development rate, total population rate, and forest region inclusion. We examined this dataset to decide the patterns in woodland region inclusion across various nations and mainlands, as well as the connection among populace and backwoods region inclusion. Our discoveries show that while certain nations have essentially expanded their forest region inclusion, others have encountered a decline. Besides, we found that population density and development rate are adversely related with forest area coverage. Authors have implemented four machine learning algorithms that are Linear Regression, Decision Tree, Random Forest and Support Vector Machine on the dataset.

References

Vancutsem C, Achard F, Pekel JF, Vieilledent G, Carboni S, Simonetti D, Gallego J, Aragao LE, Nasi R. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Science Advances. 2021 ;7(10):eabe1603.

DeFries RS, Rudel T, Uriarte M, Hansen M. Deforestation driven by urban population growth and agricultural trade in the twenty-first century. Nature Geoscience. 2010 ;3(3):178-81.

FAO F. Agriculture Organization: Global Forest Resources Assessment. FAO, Rome, Italy. 2010.

Abdulqader AW. Predicting Carbon Dioxide Emissions with the Orange Application: An Empirical Analysis. Mesopotamian Journal of Computer Science. 2023 ;2023:56-66.

JAIKISHUN S, Ansari AA, DASILVA P, HOSEN A. Carbon storage potential of mangrove forest in Guyana. International Journal of Bonorowo Wetlands. 2017;7(1):43-54.

Kucsicsa G, Popovici EA, Bălteanu D, Dumitraşcu M, Grigorescu I, Mitrică B. Assessing the potential future forest-cover change in Romania, predicted using a scenario-based modelling. Environmental Modeling & Assessment. 2020 ;25:471-91.

Li J, Zhou A, Liao Y, Zhao Z, Mao X, Zhang S. Forest ecological diversity change prediction discrete dynamic model. Discrete Dynamics in Nature and Society. 2022 ;2022:1-1.

Pecchi M, Marchi M, Moriondo M, Forzieri G, Ammoniaci M, Bernetti I, Bindi M, Chirici G. Potential impact of climate change on the forest coverage and the spatial distribution of 19 key forest tree species in Italy under RCP4. 5 IPCC trajectory for 2050s. Forests. 2020 ;11(9):934.

Reddy SR, Malathi P. Linear Regression and Artificial Neural Networks based Efficient Sales Forecasting Model with Increased Prediction Accuracy. In2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA) 2022 (pp. 1100-1105). IEEE.

C. G. Raju, V. Amudha and S. G.: Comparison of Linear Regression and Logistic Regression Algorithms for Ground Water Level Detection with Improved Accuracy. Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India, 2023, pp. 1-6.

Cao Z, Liu L, Markowitch O. Comment on “highly efficient linear regression outsourcing to a cloud”. IEEE Transactions on Cloud Computing. 2017 6;7(3):893-.

Zhang B. Tactical decision system of table tennis match based on C4. 5 decision tree. In2021 13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) 2021 (pp. 632-635). IEEE.

Mitrofanov S, Semenkin E. An approach to training decision trees with the relearning of nodes. In2021 International Conference on Information Technologies (InfoTech) 2021 (pp. 1-5). IEEE.

Yang FJ. An extended idea about decision trees. In2019 International Conference on Computational Science and Computational Intelligence (CSCI) 2019 (pp. 349-354). IEEE.

Euldji R, Boumahdi M, Bachene M. Decision-making based on decision tree for ball bearing monitoring. In2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH) 2021 (pp. 171-175). IEEE.

Miah MO, Khan SS, Shatabda S, Farid DM. Improving detection accuracy for imbalanced network intrusion classification using cluster-based under-sampling with random forests. In2019 1st international conference on advances in science, engineering and robotics technology (ICASERT) 2019 (pp. 1-5). IEEE.

Guo Y, Zhou Y, Hu X, Cheng W. Research on recommendation of insurance products based on random forest. In2019 international conference on machine learning, big data and business intelligence (MLBDBI) 2019 (pp. 308-311). IEEE.

Sapra V, Sapra L, Vishnoi A, Srivastava P. Identification of Brain Stroke using Boosted Random Forest. In2022 International Conference on Advances in Computing, Communication and Materials (ICACCM) 2022 (pp. 1-5). IEEE.

Zhili C, Guiyan J. Application of multiple SVM classifier fusion technique in freeway automatic incident detection. In2008 27th Chinese Control Conference 2008 (pp. 581-585). IEEE.

Yang Y, Wang J, Yang Y. Exploiting rotation invariance with SVM classifier for microcalcification detection. In 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) 2012 (pp. 590-593). IEEE.

Rao NS, Thangaraj SJ, Kumari VS. Flight Ticket Prediction Using Gradient Boosting Regressor Compared With Linear Regression. In 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) 2023 (pp. 1-6). IEEE.

Rasoarahona R, Wattanadilokchatkun P, Panthum T, Thong T, Singchat W, Ahmad SF, Chaiyes A, Han K, Kraichak E, Muangmai N. Optimizing Microsatellite Marker Panels for Genetic Diversity and Population Genetic Studies: An Ant Colony Algorithm Approach with Polymorphic Information Content. Biology 2023; 12, 1280.

Arvindhan M, Rajeshkumar D, Pal AL. A review of challenges and opportunities in machine learning for healthcare. Exploratory Data Analytics for Healthcare. 2021; pp 67-84.

Chandraprabha M, Dhanaraj RK. Machine learning based Pedantic Analysis of Predictive Algorithms in Crop Yield Management. In2020 4th International conference on electronics, communication and aerospace technology (ICECA) 2020 (pp. 1340-1345). IEEE.

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Published

15-02-2024

How to Cite

1.
Metkewar PS, Chauhan R, Prasanth A, Sathyamoorthy M. Assessment of Global Forest Coverage through Machine Learning Algorithms . EAI Endorsed Scal Inf Syst [Internet]. 2024 Feb. 15 [cited 2024 Jul. 22];11(4). Available from: https://publications.eai.eu/index.php/sis/article/view/5122